title: Modeling nonlinear gene regulatory networks from time series gene expression data.
abstract: In cells, molecular networks such as gene regulatory networks are the basis of biological complexity. Therefore, gene regulatory networks have become the core of research in systems biology. Understanding the processes underlying the several extracellular regulators, signal transduction, protein-protein interactions, and differential gene expression processes requires detailed molecular description of the protein and gene networks involved. To understand better these complex molecular networks and to infer new regulatory associations, we propose a statistical method based on vector autoregressive models and Granger causality to estimate nonlinear gene regulatory networks from time series microarray data. Most of the models available in the literature assume linearity in the inference of gene connections; moreover, these models do not infer directionality in these connections. Thus, a priori biological knowledge is required. However, in pathological cases, no a priori biological information is available. To overcome these problems, we present the nonlinear vector autoregressive (NVAR) model. We have applied the NVAR model to estimate nonlinear gene regulatory networks based entirely on gene expression profiles obtained from DNA microarray experiments. We show the results obtained by NVAR through several simulations and by the construction of three actual gene regulatory networks (p53, NF-kappaB, and c-Myc) for HeLa cells.
authors.affiliation: Human Genome Center, University of Tokyo, 4-6-1 Shirokanedai, Tokyo 108-8639, Japan. afujita@ims.u-tokyo.ac.jp
authors.forename: André
authors.initials: A
authors.lastname: Fujita
authors.forename: João Ricardo
authors.initials: JR
authors.lastname: Sato
authors.forename: Humberto Miguel
authors.initials: HM
authors.lastname: Garay-Malpartida
authors.forename: Mari Cleide
authors.initials: MC
authors.lastname: Sogayar
authors.forename: Carlos Eduardo
authors.initials: CE
authors.lastname: Ferreira
authors.forename: Satoru
authors.initials: S
authors.lastname: Miyano
mesh_terms: D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D008954:Models, Biological; D017711:Nonlinear Dynamics; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D020543:Proteome